class_docstr: Anomalib dataset.
  
  Args:
      task (str): Task type, either 'classification' or 'segmentation'
      transform (A.Compose): Albumentations Compose object describing the transforms that are applied to the inputs.
docstr: Get dataset item for the index ``index``.
  
  Args:
      index (int): Index to get the item.
  
  Returns:
      Union[dict[str, Tensor], dict[str, str | Tensor]]: Dict of image tensor during training.
          Otherwise, Dict containing image path, target path, image tensor, label and transformed bounding box.
docstr_tok: ["Get","dataset","item","for","the","index","``","index","``",".","Args",":","index","(","int",")",":","Index","to","get","the","item",".","Returns",":","Union","[","dict","[","str",",","Tensor","]",",","dict","[","str",",","str","|","Tensor","]","]",":","Dict","of","image","tensor","during","training",".","Otherwise",",","Dict","containing","image","path",",","target","path",",","image","tensor",",","label","and","transformed","bounding","box","."]
code: def __getitem__(self, index: int) -> dict[str, str | Tensor]:
      """Get dataset item for the index ``index``.
  
      Args:
          index (int): Index to get the item.
  
      Returns:
          Union[dict[str, Tensor], dict[str, str | Tensor]]: Dict of image tensor during training.
              Otherwise, Dict containing image path, target path, image tensor, label and transformed bounding box.
      """
  
      image_path = self._samples.iloc[index].image_path
      mask_path = self._samples.iloc[index].mask_path
      label_index = self._samples.iloc[index].label_index
  
      image = read_image(image_path)
      item = dict(image_path=image_path, label=label_index)
  
      if self.task == TaskType.CLASSIFICATION:
          transformed = self.transform(image=image)
          item["image"] = transformed["image"]
      elif self.task in (TaskType.DETECTION, TaskType.SEGMENTATION):
          # Only Anomalous (1) images have masks in anomaly datasets
          # Therefore, create empty mask for Normal (0) images.
  
          if label_index == 0:
              mask = np.zeros(shape=image.shape[:2])
          else:
              mask = cv2.imread(mask_path, flags=0) / 255.0
  
          transformed = self.transform(image=image, mask=mask)
  
          item["image"] = transformed["image"]
          item["mask_path"] = mask_path
          item["mask"] = transformed["mask"]
  
          if self.task == TaskType.DETECTION:
              # create boxes from masks for detection task
              boxes, _ = masks_to_boxes(item["mask"])
              item["boxes"] = boxes[0]
      else:
          raise ValueError(f"Unknown task type: {self.task}")
  
      return item
  
imports: from __future__ import annotations
  import copy
  import logging
  from abc import ABC, abstractmethod
  from pathlib import Path
  from typing import Sequence
  import albumentations
  import cv2
  import numpy
  import pandas
  from pandas import DataFrame
  from torch import Tensor
  from torch.utils.data import Dataset
  from anomalib.data.task_type import TaskType
  from anomalib.data.utils import masks_to_boxes, read_image
label_desc: Use image node_id 9 for calling the AnomalibDataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: item